Accuracy of the SEVEN continuous glucose monitoring system: comparison with frequently sampled venous glucose measurements

Comparator Data Characteristics and Testing Procedures for the Clinical Performance Evaluation of Continuous Glucose Monitoring Systems

Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are <70 mg/dL (3.9 mmol/L) and >300 mg/dL (16.7 mmol/L), respectively, and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels are suggested that enable the collection of comparator data with these characteristics. This work is an important step toward establishing a future standard for the performance evaluation of CGM systems.

Accuracy and Safety of the 15-Day CareSens Air Continuous Glucose Monitoring System

Background: We evaluated the accuracy and safety of the CareSens Air, a novel real-time continuous glucose monitoring system (CGMS), during 15 days of use in adults with diabetes. Methods: Adults with either type 1 diabetes or type 2 diabetes requiring intensive insulin therapy participated at four sites in South Korea. All participants wore the sensor for 15 days. Participants were scheduled for four 8-h clinic sessions on Day 1, 5 ± 1, 10 ± 1, and 15. Accuracy was evaluated based on the proportion of continuous glucose monitoring (CGM) values within 15% of YSI values ≥100 mg/dL or within 15 mg/dL of YSI values <100 mg/dL (%15/15), along with the %20/20, %30/30, and %40/40 agreement rates. The mean absolute relative difference (MARD) between the CGM and YSI values was calculated. Results: Data from 83 participants (83 sensors, 10,029 CGM-YSI matched pairs) were analyzed. The overall MARD was 10.42%, and the overall %15/15, %20/20, %30/30, and %40/40 accuracy were 78.55%, 89.04%, 96.47%, and 98.87%, respectively. The consensus error grid analysis showed that 99.92% of CGM values fell into Zone A or B (Zone A: 89.83%, Zone B: 10.09%). The %20/20 accuracy of CGMS was 88.11% on Day 1, 90.11% on Day 3-5, 92.09% on Day 8-10, and 85.63% on Day 15. No serious adverse events were reported. Conclusions: The CareSens Air demonstrated accurate performance across the wide glycemic range and was well tolerated during the 15-day sensor use period.

Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.

Emerging Diabetes Technologies: Continuous Glucose Monitors/Artificial Pancreases

Over the past decade there have been many advances in diabetes technologies, such as continuous glucose monitors (CGM s), insulin-delivery devices, and hybrid closed loop systems . Now most CGMs (Medtronic-Guardian, Dexcom-G6, and Abbott-Libre-2) have MARD values of < 10%, in contrast to two decades ago when the MARD used to be > 20%. In addition, the majority of the new CGMs do not require calibrations, and the latest CGMs last for 10-14 days. An implantable 6-months CGM by Eversense-3 is now approved in the USA and Europe. Recently, the FDA approved Libre 3 which provides real-time glucose values every minute. Even though it is approved as an iCGM it is not interoperable with automatic-insulin-delivery (AID) systems. The newer CGMs that are likely to be launched in the next few months in the USA include the 10-11 days Dexcom G7 (60% smaller than the existing G6), and the 7-days Medtronic Guardian 4. Most of the newer CGM have several features like automatic initialization, easy insertion, predictive alarms, and alerts. It has also been noticed that an arm insertion site might have better accuracy than abdomen or other sites, like the buttock for kids. Lag time between YSI and different sensors have been reported differently, sometimes it is down to 2-3 min; however, in many instances, it is still 15-20 min, especially when the rate of change of glucose is > 2 mg/min. We believe that in the next decade there will be a significant increase in the number of people who use CGM for their day-to-day diabetes care.

Should continuous glucose monitoring be used to manage neonates at risk of hypoglycaemia?

The National Institute for Clinical Excellence (NICE) now recommends that continuous glucose monitoring (CGM) be offered to adults and children with diabetes who are at risk from hypoglycaemia. Hypoglycaemia is common in the neonatal period, and is a preventable cause of poor neurodevelopmental outcome, but is CGM helpful in the management of neonates at risk of hypoglycaemia? Neonatal studies have shown that CGM can detect clinically silent hypoglycaemia, which has been associated with reduced executive and visual function in early childhood. Intervention trials have further shown CGM can support the targeting of glucose levels in high-risk extremely preterm neonates. In spite of significant advances in technology, including smaller sensors, better accuracy and factory calibration, further progress and adoption into clinical practice has been limited as current devices are not designed nor have regulatory approval for the specific needs of the newborn. The use of CGM has the potential to support clinical management, and prevention of hypoglycaemia but must be set within its current limitations. The data CGM provides however also provides an important opportunity to improve our understanding of potential risks of hypoglycaemia and the impact of clinical interventions to prevent it.

Continuous glucose monitoring as a close to real life alternative to meal studies – a pilot study with a functional drink containing amino acids and chromium

This pilot study aimed to evaluate a continuous glucose monitoring (CGM) based approach to study the effects of a functional drink containing specific amino acids and chromium picolinate (FD) and a combination of FD with a juice (FDJ) on postprandial glucose in a close to real life setting. The predefined primary endpoint for this study was the 120-min incremental area under the glucose curve (iAUC0−120min) after meals. It was estimated that using CGM and repeated meals in 6 participants could be sufficient to match the power of the previous study in regards to the quantity of meals. Participants followed a pre-specified meal schedule over 9 days and consumed the drinks three times daily with main meals. Differences between drinks were analyzed by analysis of covariances (ANCOVA) with subject number and activity as random factors and nutrient composition as covariates. In 156 meals available for analysis, a significant 34% reduction of glucose iAUC0−120min was shown for FDJ (p &lt; 0.001). FD did not show a significant effect on its own, but a significant reduction of 17.6% (p = 0.007) was shown in pooled data for FD and FDJ. While the differences between the two functional drinks used were not the primary focus of this study, it was sufficiently powered to detect previously described effects in 60 participants in a cross-over design under laboratory settings. The design presented defines a novel and cost-effective approach using CGM devices and app-based lifestyle tracking for studying nutritional effects on glucose at home in a close to real-life setting.

Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology—A Review

The past few decades have seen ongoing development of continuous glucose monitoring (CGM) systems that are noninvasive and accurately measure blood glucose levels. The conventional finger-prick method, though accurate, is not feasible for use multiple times a day, as it is painful and test strips are expensive. Although minimally invasive and noninvasive CGM systems have been introduced into the market, they are expensive and require finger-prick calibrations. As the diabetes trend is high in low- and middle-income countries, a cost-effective and easy-to-use noninvasive glucose monitoring device is the need of the hour. This review paper briefly discusses the noninvasive glucose measuring technologies and their related research work. The technologies discussed are optical, transdermal, and enzymatic. The paper focuses on Near Infrared (NIR) technology and NIR Photoplethysmography (PPG) for blood glucose prediction. Feature extraction from PPG signals and glucose prediction with machine learning methods are discussed. The review concludes with key points and insights for future development of PPG NIR-based blood glucose monitoring systems.

Comparison of the clinical effects of intermittently scanned and real-time continuous glucose monitoring in children and adolescents with type 1 diabetes: A retrospective cohort study

AIMS

/Introduction The aim of the study was to compare two continuous glucose monitoring (CGM) systems, intermittently scanned CGM (isCGM) and real-time CGM (rtCGM), to determine which system achieved better glycemic control in pediatric patients.

MATERIALS AND METHODS

We conducted a retrospective cohort study of children and adolescents with type 1 diabetes and compared the time in range (TIR) (70-180 mg/dL), time below range (TBR) (< 70 mg/dL), and time above range (TAR) (> 180 mg/dL), and estimated HbA1c (eA1c) levels between patients on isCGM and rtCGM.

RESULTS

Of the 112 participants, 76 (67.9%) used isCGM and 36 (32.1%) used rtCGM for glycemic management. Patients on rtCGM had significantly greater TIR (57.7% ± 12.3% vs. 52.3% ± 12.3%, P = 0.0368), and had significantly lower TBR (4.3% ± 2.7% vs. 10.2% ± 5.4%, P < 0.001) than those on isCGM but there was no significant difference in the TAR (37.4% ± 12.9% vs. 38.0% ± 12.5%, P = 0.881) or the eA1c levels (7.4% ± 0.9% vs. 7.5% ± 0.8%, P = 0.734) between the two groups.

CONCLUSIONS

Pediatric patients with type 1 diabetes on rtCGM also showed more beneficial effects for increase of TIR with notably reduction of TBR than those on isCGM. Real-time CGM may provide better glycemic control than isCGM in children with type 1 diabetes.

In Vivo Monitoring of Glucose Using Ultrasound-Induced Resonance in Implantable Smart Hydrogel Microstructures

A novel glucose sensor is presented using smart hydrogels as biocompatible implantable sensing elements, which eliminates the need for implanted electronics and uses an external medical-grade ultrasound transducer for readout. The readout mechanism uses resonance absorption of ultrasound waves in glucose-sensitive hydrogels. In vivo glucose concentration changes in the interstitial fluid lead to swelling or deswelling of the gels, which changes the resonance behavior. The hydrogels are designed and shaped such as to exhibit specific mechanical resonance frequencies while remaining sonolucent to other frequencies. Thus, they allow conventional and continued ultrasound imaging, while yielding a sensing signal at specific frequencies that correlate with glucose concentration. The resonance frequencies can be tuned by changing the shape and mechanical properties of the gel structures, such as to allow for multiple, colocated implanted hydrogels with different sensing characteristics or targets to be employed and read out, without interference using the same ultrasound transducer, by simply toggling frequencies. The fact that there is no need for any implantable electronics, also opens up the path toward future use of biodegradable hydrogels, thus creating a platform that allows injection of sensors that do not need to be retrieved when they reach the end of their useful lifespan.

Proof of Concept for a New Raman-Based Prototype for Noninvasive Glucose Monitoring

BACKGROUND

Noninvasive glucose monitoring (NIGM) in diabetes is a long-sought-for technology. Among the many attempts Raman spectroscopy was considered as the most promising because of its glucose specificity. In this study, a recently developed prototype (GlucoBeam, RSP Systems A/S, Denmark) was tested in patients with type 1 diabetes to establish calibration models and to demonstrate proof of concept for this device in real use.

METHODS

The NIGM table-top prototype was used by 15 adult subjects with type 1 diabetes for up to 25 days at home and in an in-clinic setting. On each day, the subjects performed at least six measurement units throughout the day. Each measurement unit comprised two capillary blood glucose measurements, two scans with an intermittent scanning continuous glucose monitoring (CGM) system, and two NIGM measurements using the thenar of the subject's right hand.

RESULTS

Calibration models were established using data from 19 to 24 days. The remaining 3-8 days were used for independent validation. The mean absolute relative difference of the NIGM prototype was 23.6% ± 13.1% for the outpatient days, 28.2% ± 9.9% for the in-clinic day, and 26.3% ± 10.8% for the complete study. Consensus error grid analysis of the NIGM prototype for the complete study showed 93.6% of values in clinically acceptable zones A and B.

CONCLUSIONS

This proof of concept study demonstrated a practical realization of a Raman-based NIGM device, with performance on par with early-generation CGM systems. The findings will assist in further performance improvements of the device.

Differences in glucose level between right arm and left arm using continuous glucose monitors

Background

Continuous glucose monitoring (CGM) measures interstitial glucose levels through a sensor with a thin filament inserted under the skin. It is customary for patients to rotate sensor application sites between arms to minimize skin irritation. However, there is limited data regarding the degree of inter-arm differences with CGM technology.

Methods

Self-proclaimed right-handed (n = 5) and left-handed (n = 5) participants, regardless of concurrent comorbidities, were enrolled for CGM. Participants wore a FreeStyle Libre Pro sensor on each arm for a maximum of 14 days. Muscle mass and body fat analysis was conducted using a multi-frequency segmental body composition analyzer. Glucose levels from both arms were time-matched with the first 12 hours eliminated from analysis. Mean glucose and time in target range were compared between readings from the right and left arm.

Results

A total of 9830 paired glucose levels were included for analysis. In all participants (n = 10), mean glucose on the right arm was 89.1 mg/dL (SD, 19.9) and 85.3 mg/dL (SD, 19.3) on the left arm (P < 0.001). Glucose was out of target range (70-180 mg/dL) for 12.7% of the time in the right arm compared to 18.5% in the left arm (P < 0.001).

Conclusions

In a group of 10 nondiabetic and diabetic adults, there was a statistically significant difference in CGM readings between the right and left arms. Time in target range may differ based on arm selection when using a CGM. Arm dominance did not explain the inter-arm glucose level discordance.

Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications

By providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors.

Measures of Accuracy for Continuous Glucose Monitoring and Blood Glucose Monitoring Devices

Currently, patients with diabetes may choose between two major types of system for glucose measurement: blood glucose monitoring (BGM) systems measuring glucose within capillary blood and continuous glucose monitoring (CGM) systems measuring glucose within interstitial fluid. Although BGM and CGM systems offer different functionality, both types of system are intended to help users achieve improved glucose control. Another area in which BGM and CGM systems differ is measurement accuracy. In the literature, BGM system accuracy is assessed mainly according to ISO 15197:2013 accuracy requirements, whereas CGM accuracy has hitherto mainly been assessed by MARD, although often results from additional analyses such as bias analysis or error grid analysis are provided. The intention of this review is to provide a comparison of different approaches used to determine the accuracy of BGM and CGM systems and factors that should be considered when using these different measures of accuracy to make comparisons between the analytical performance (ie, accuracy) of BGM and CGM systems. In addition, real-world implications of accuracy and its relevance are discussed.

Measurement Performance of Two Continuous Tissue Glucose Monitoring Systems Intended for Replacement of Blood Glucose Monitoring

BACKGROUND

Currently, two systems for continuous tissue glucose monitoring (CGM) (Dexcom^® G5 [DG5] and FreeStyle Libre [FL]) are intended to replace blood glucose monitoring (BGM) and, according to manufacturer labeling, are distributed as such in some jurisdictions, including the United States and the European Union.

METHODS

The measurement performance of these two systems in comparison with a BGM system was analyzed in a 14-day study with 20 participants comprising study site visits, which included phases of induced rapid glucose changes, and home use phases. Performance analysis was mainly based on deviations between CGM readings and BGM results. Sensor-to-sensor precision was also analyzed.

RESULTS

Approximately 25% of DG5 and FL results showed differences from BGM results exceeding 15 mg/dL or 15% (at glucose concentration below or above 100 mg/dL, respectively) at times of therapeutic decisions, and ∼5% of differences exceeded 30 mg/dL or 30%. Performance was different depending on the setting (study site visits, home use phases, and phases of induced rapid glucose changes). In consensus error grid (CEG) analysis, both systems showed >99.5% of results within the clinically acceptable zones A and B.

CONCLUSIONS

In this study, both systems showed deviations from blood glucose (BG) measurements, the current standard approach in diabetes therapy. Although a large percentage of results was found in CEG zones A and B, for approximately one in four therapeutic decisions, CGM and BG readings differed by at least 15 mg/dL or 15%. Such deviations should be taken into account when using CGM systems.

Interstitial Glucose and Lactate Levels Are Inversely Correlated With the Body Mass Index: Need for In Vivo Calibration of Glucose Sensor Results With Blood Values in Obese Patients

BACKGROUND

Continuously measured glucose and lactate levels in interstitial fluid (ISF) may markedly differ from their respective blood levels.

METHODS

Combining microdialysis with a bioanalytical microsystem, the interstitial glucose and lactate concentrations of eight male volunteers with different body mass index (BMI) were monitored during a 2-fold glucose tolerance test over the period of three hours.

RESULTS

Significant correlations were found between abdominally measured sensor results and reference measurements ( R2 = .967 for glucose and R2 = .936 for lactate, P < .05). The physiological delay of the abdominally observed glucose appearance in the ISF correlated positively with the BMI ( R2 = .787, P < .05). The relative in vivo recovery of glucose and lactate was inversely proportional to the BMI of the volunteers ( R2 = .540 for glucose, R2 = .609 for lactate, P < .05). One subject with a BMI of > 34 kg/m2 showed abdominally as well as the antebrachially significantly reduced tissue glucose values compared to blood glucose values ( P < .001).

CONCLUSIONS

A very good correlation between abdominally measured sensor results and the results of the reference method verified the reliability of the BioMEMS. The abdominally measured glucose level in ISF decreased significantly with increasing BMI. Therefore, an in vivo calibration of glucose levels in ISF with blood levels seems to be necessary especially in markedly obese subjects.

Autoregressive Modeling of Drift and Random Error to Characterize a Continuous Intravascular Glucose Monitoring Sensor

BACKGROUND

Continuous glucose monitoring (CGM) devices have been effective in managing diabetes and offer potential benefits for use in the intensive care unit (ICU). Use of CGM devices in the ICU has been limited, primarily due to the higher point accuracy errors over currently used traditional intermittent blood glucose (BG) measures. General models of CGM errors, including drift and random errors, are lacking, but would enable better design of protocols to utilize these devices. This article presents an autoregressive (AR) based modeling method that separately characterizes the drift and random noise of the GlySure CGM sensor (GlySure Limited, Oxfordshire, UK).

METHODS

Clinical sensor data (n = 33) and reference measurements were used to generate 2 AR models to describe sensor drift and noise. These models were used to generate 100 Monte Carlo simulations based on reference blood glucose measurements. These were then compared to the original CGM clinical data using mean absolute relative difference (MARD) and a Trend Compass.

RESULTS

The point accuracy MARD was very similar between simulated and clinical data (9.6% vs 9.9%). A Trend Compass was used to assess trend accuracy, and found simulated and clinical sensor profiles were similar (simulated trend index 11.4° vs clinical trend index 10.9°).

CONCLUSION

The model and method accurately represents cohort sensor behavior over patients, providing a general modeling approach to any such sensor by separately characterizing each type of error that can arise in the data. Overall, it enables better protocol design based on accurate expected CGM sensor behavior, as well as enabling the analysis of what level of each type of sensor error would be necessary to obtain desired glycemic control safety and performance with a given protocol.

A Comparison of Time Delay in Three Continuous Glucose Monitors for Adolescents and Adults

BACKGROUND

The physiologic delay in glucose diffusion from the blood to the interstitial fluid and instrumental factors contribute to the delay between changes in plasma glucose (PG) and measurements made by continuous glucose monitors (CGMs). This study compared the duration of this delay for three CGMs.

METHODS

A total of 24 healthy adolescent and adult subjects with type 1 diabetes wore three CGM devices simultaneously for 48 hours: Dexcom G4 Platinum, Abbott Navigator, and Medtronic Enlite. The time delay between PG and CGM-estimated plasma glucose (CGMG) was estimated by comparing time-shifted CGMG with reference PG taken every 15 minutes.

RESULTS

The delay estimated by our approach was larger for the Navigator than for the G4 Platinum in adolescents (7.7 ± 1.1 versus 5.6 ± 0.9 min, P = .0396) and adults (10.9 ± 1.1 versus 8.1 ± 0.7 min, P = .0107). The delay was nominally longer for the Navigator than for the Enlite in both the adolescent (7.7 ± 1.1 versus 4.3 ± 1.0 min, P = .0728) and adult (10.9 ± 1.1 versus 8.3 ± 0.9 min, P = .111) populations, but these differences were not statistically significant. There was no difference in the delay between G4 Platinum and Enlite. Adolescents had shorter delays than adults for all three devices. There was a significant correlation between longer delay and increasing age for the G4 Platinum and Navigator.

CONCLUSIONS

There are differences in the estimated PG to CGMG time delays between CGM devices in the same subjects. The delay between PG and CGMG is smaller for adolescents than for adults. The PG-to-CGMG time delay is influenced by both instrument and host factors.

Extensive Assessment of Blood Glucose Monitoring During Postprandial Period and Its Impact on Closed-Loop Performance

BACKGROUND

Closed-loop (CL) systems aims to outperform usual treatments in blood glucose control and continuous glucose monitors (CGM) are a key component in such systems. Meals represents one of the main disturbances in blood glucose control, and postprandial period (PP) is a challenging situation for both CL system and CGM accuracy.

METHODS

We performed an extensive analysis of sensor's performance by numerical accuracy and precision during PP, as well as its influence in blood glucose control under CL therapy.

RESULTS

During PP the mean absolute relative difference (MARD) for both sensors presented lower accuracy in the hypoglycemic range (19.4 ± 12.8%) than in other ranges (12.2 ± 8.6% in euglycemic range and 9.3 ± 9.3% in hyperglycemic range). The overall MARD was 12.1 ± 8.2%. We have also observed lower MARD for rates of change between 0 and 2 mg/dl. In CL therapy, the 10 trials with the best sensor spent less time in hypoglycemia (PG < 70 mg/dl) than the 10 trials with the worst sensors (2 ± 7 minutes vs 32 ± 38 minutes, respectively).

CONCLUSIONS

In terms of accuracy, our results resemble to previously reported. Furthermore, our results showed that sensors with the lowest MARD spent less time in hypoglycemic range, indicating that the performance of CL algorithm to control PP was related to sensor accuracy.

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine.

Evaluation of subcutaneous glucose monitoring systems under routine environmental conditions in patients with type 1 diabetes

Continuous and flash glucose monitoring (GM) systems have been established in diabetes care. We compared the sensor performance of 3 commercially available GM systems. A total of 12 patients with type 1 diabetes were included in a single-centre, open-label study in which the sensor performance of the Abbott FreeStyle libre (Abbott), Dexcom G4 Platinum (Dexcom) and Medtronic MiniMed 640G (Medtronic) systems over 12 hours was compared during mimicked real-life conditions (meals, exercise, hypo- and hyperglycaemia). Sensor performance was determined by fulfilment of ISO 15197:2013 criteria, calculating mean absolute relative difference (MARD), and was also illustrated using Parkes error grid and Bland-Altman plots. Sensor performance during changes in metabolic variables (lactate, betahydroxybutyrate, glucagon, non-esterified-fatty-acids) was determined by Spearman's rank correlation coefficient testing. The systems fulfilled ISO 15197:2013 criteria by 73.2% (Abbott), 56.1% (Dexcom) and 52.0% (Medtronic). The MARDs ± standard deviation in the entire glycaemic range were 13.2% ± 10.9% (Abbott), 16.8% ± 12.3% (Dexcom) and 21.4% ± 17.6% (Medtronic), respectively. All sensors performed less accurately during hypoglycaemia and best during hyperglycaemia. We did not observe an influence of metabolic variables on sensor performance.

REPLACE-BG: A Randomized Trial Comparing Continuous Glucose Monitoring With and Without Routine Blood Glucose Monitoring in Adults With Well-Controlled Type 1 Diabetes

OBJECTIVE

To determine whether the use of continuous glucose monitoring (CGM) without confirmatory blood glucose monitoring (BGM) measurements is as safe and effective as using CGM adjunctive to BGM in adults with well-controlled type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

A randomized noninferiority clinical trial was conducted at 14 sites in the T1D Exchange Clinic Network. Participants were ≥18 years of age (mean 44 ± 14 years), had T1D for ≥1 year (mean duration 24 ± 12 years), used an insulin pump, and had an HbA_1c ≤9.0% (≤75 mmol/mL) (mean 7.0 ± 0.7% [53 ± 7.7 mmol/mol]); prestudy, 47% were CGM users. Participants were randomly assigned 2:1 to the CGM-only (n = 149) or CGM+BGM (n = 77) group. The primary outcome was time in range (70-180 mg/dL) over the 26-week trial, with a prespecified noninferiority limit of 7.5%.

RESULTS

CGM use averaged 6.7 ± 0.5 and 6.8 ± 0.4 days/week in the CGM-only and CGM+BGM groups, respectively, over the 26-week trial. BGM tests per day (including the two required daily for CGM calibration) averaged 2.8 ± 0.9 and 5.4 ± 1.4 in the two groups, respectively (P < 0.001). Mean time in 70-180 mg/dL was 63 ± 13% at both baseline and 26 weeks in the CGM-only group and 65 ± 13% and 65 ± 11% in the CGM+BGM group (adjusted difference 0%; one-sided 95% CI -2%). No severe hypoglycemic events occurred in the CGM-only group, and one occurred in the CGM+BGM group.

CONCLUSIONS

Use of CGM without regular use of confirmatory BGM is as safe and effective as using CGM with BGM in adults with well-controlled T1D at low risk for severe hypoglycemia.

Redundancy in Glucose Sensing: Enhanced Accuracy and Reliability of an Electrochemical Redundant Sensor for Continuous Glucose Monitoring

BACKGROUND

Current electrochemical glucose sensors use a single electrode. Multiple electrodes (redundancy) may enhance sensor performance. We evaluated an electrochemical redundant sensor (ERS) incorporating two working electrodes (WE1 and WE2) onto a single subcutaneous insertion platform with a processing algorithm providing a single real-time continuous glucose measure.

METHODS

Twenty-three adults with type 1 diabetes each wore two ERSs concurrently for 168 hours. Post-insertion a frequent sampling test (FST) was performed with ERS benchmarked against a glucose meter (Bayer Contour Link). Day 4 and 7 FSTs were performed with a standard meal and venous blood collected for reference glucose measurements (YSI and meter). Between visits, ERS was worn with capillary blood glucose testing ≥8 times/day. Sensor glucose data were processed prospectively.

RESULTS

Mean absolute relative deviation (MARD) for ERS day 1-7 (3,297 paired points with glucose meter) was (mean [SD]) 10.1 [11.5]% versus 11.4 [11.9]% for WE1 and 12.0 [11.9]% for WE2; P < .0001. ERS Clarke A and A+B were 90.2% and 99.8%, respectively. ERS day 4 plus day 7 MARD (1,237 pairs with YSI) was 9.4 [9.5]% versus 9.6 [9.7]% for WE1 and 9.9 [9.7]% for WE2; P = ns. ERS day 1-7 precision absolute relative deviation (PARD) was 9.9 [3.6]% versus 11.5 [6.2]% for WE1 and 10.1 [4.4]% for WE2; P = ns. ERS sensor display time was 97.8 [6.0]% versus 91.0 [22.3]% for WE1 and 94.1 [14.3]% for WE2; P < .05.

CONCLUSIONS

Electrochemical redundancy enhances glucose sensor accuracy and display time compared with each individual sensing element alone. ERS performance compares favorably with 'best-in-class' of non-redundant sensors.

Rate-of-Change Dependence of the Performance of Two CGM Systems During Induced Glucose Swings

INTRODUCTION

The accuracy of continuous glucose monitoring (CGM) systems is often assessed with respect to blood glucose (BG) readings. CGM readings are affected by a physiological and a technical time delay when compared to BG readings. In this analysis, the dependence of CGM performance parameters on the BG rate of change was investigated for 2 CGM systems.

METHODS

Data from a previously published study were retrospectively analyzed. An established CGM system (Dexcom G4, Dexcom, San Diego, CA; system A) and a prototype system (Roche Diagnostics GmbH, Mannheim, Germany; system B) with 2 sensors each were worn by 10 subjects in parallel. Glucose swings were induced to achieve rapidly changing BG concentrations. Mean absolute relative differences (MARD) were calculated in different BG rate-of-change categories. In addition, sensor-to-sensor precision was assessed.

RESULTS

At BG rates of change of -1 mg/dl/min to 0 mg/dl/min and 0 mg/dl/min to +1 mg/dl/min, MARD results were 12.6% and 11.3% for system A and 8.2% and 10.0% for system B. At rapidly changing BG concentrations (<-3 mg/dl/min and ≥+3 mg/dl/min), higher MARD results were found for both systems, but system B was less affected (system A: 24.9% and 29.6%, system B: 10.6% and 16.3%). The impact of rate of change on sensor-to-sensor precision was less pronounced.

CONCLUSIONS

Both systems were affected by rapidly changing BG concentrations to some degree, although system B was mostly unaffected by decreasing BG concentrations. It would seem that technological advancements in CGM systems might allow for a more precise tracking of BG concentrations even at rapidly changing BG concentrations.

Use of microdialysis-based continuous glucose monitoring to drive real-time semi-closed-loop insulin infusion

Continuous glucose monitoring (CGM) and automated insulin delivery may make diabetes management substantially easier, if the quality of the resulting therapy remains adequate. In this study, a semi-closed-loop control algorithm was used to drive insulin therapy and its quality was compared to that of subject-directed therapy. Twelve subjects stayed at the study site for approximately 70 hours and were provided with the investigational Automated Pancreas System Test Stand (APS-TS), which was used to calculate insulin dosage recommendations automatically. These recommendations were based on microdialysis CGM values and common diabetes therapy parameters. For the first half of their stay, the subjects directed their diabetes therapy themselves, whereas for the second half, the insulin recommendations were delivered by the APS-TS (so-called algorithm-driven therapy). During subject-directed therapy, the mean glucose was 114 mg/dl compared to 125 mg/dl during algorithm-driven therapy. Time in target (90 to 150 mg/dl) was approximately 46% during subject-directed therapy and approximately 58% during algorithm-driven therapy. When subjects directed their therapy, approximately 2 times more hypoglycemia interventions (oral administration of carbohydrates) were required than during algorithm-driven therapy. No hyperglycemia interventions (delivery of addition insulin) were necessary during subject-directed therapy, while during algorithm-driven therapy, 2 hyperglycemia interventions were necessary. The APS-TS was able to adequately control glucose concentrations in the subjects. Time in target was at least comparable or moderately higher during closed-loop control and markedly fewer hypoglycemia interventions were required, thus increasing patient safety.

The single-port concept: combining optical glucose measurement with insulin infusion

The aim of this study was to develop a single-port system that combines glucose monitoring and insulin infusion. In a preclinical proof-of-concept trial, the performance of the glucose measurement at the site of insulin infusion was assessed. Glucose levels were clamped from 40 to 250 mg/dL by intravenous glucose infusion and subcutaneous insulin infusion via the glucose sensor. Sensor-glucose values correlated well with reference blood-glucose values, despite infusion at the site of glucose measurement. The average median ARE value was 21.6 ± 5.7 % for sensors used for insulin infusion, 18.1 ± 5.8 % for sensors used for NaCl infusion and 19.2 ± 7.9 % for sensors without infusion. These preclinical in vivo results demonstrate that single-port glucose monitoring is feasible at the site of insulin infusion.

Characterizing accuracy and precision of glucose sensors and meters

There is need for a method to describe precision and accuracy of glucose measurement as a smooth continuous function of glucose level rather than as a step function for a few discrete ranges of glucose. We propose and illustrate a method to generate a "Glucose Precision Profile" showing absolute relative deviation (ARD) and /or %CV versus glucose level to better characterize measurement errors at any glucose level. We examine the relationship between glucose measured by test and comparator methods using linear regression. We examine bias by plotting deviation = (test - comparator method) versus glucose level. We compute the deviation, absolute deviation (AD), ARD, and standard deviation (SD) for each data pair. We utilize curve smoothing procedures to minimize the effects of random sampling variability to facilitate identification and display of the underlying relationships between ARD or %CV and glucose level. AD, ARD, SD, and %CV display smooth continuous relationships versus glucose level. Estimates of MARD and %CV are subject to relatively large errors in the hypoglycemic range due in part to a markedly nonlinear relationship with glucose level and in part to the limited number of observations in the hypoglycemic range. The curvilinear relationships of ARD and %CV versus glucose level are helpful when characterizing and comparing the precision and accuracy of glucose sensors and meters.

The artificial pancreas: current status and future prospects in the management of diabetes

Recent advances in insulins, insulin pumps, continuous glucose-monitoring systems, and control algorithms have resulted in an acceleration of progress in the development of artificial pancreas devices. This review discusses progress in the development of external systems that are based on subcutaneous drug delivery and subcutaneous continuous glucose monitoring. There are two major system-level approaches to achieving closed-loop control of blood glucose in diabetic individuals. The unihormonal approach uses insulin to reduce blood glucose and relies on complex safety mitigation algorithms to reduce the risk of hypoglycemia. The bihormonal approach uses both insulin to lower blood glucose and glucagon to raise blood glucose, and also relies on complex algorithms to provide for safety of the user. There are several major strategies for the design of control algorithms and supervision control for application to the artificial pancreas: proportional-integral-derivative, model predictive control, fuzzy logic, and safety supervision designs. Advances in artificial pancreas research in the first decade of this century were based on the ongoing computer revolution and miniaturization of electronic technology. The advent of modern smartphones has created the ability to utilize smartphone technology as the engineering centerpiece of an artificial pancreas. With these advances, an artificial or bionic pancreas is within reach.

Accuracy of a Novel Noninvasive Multisensor Technology to Estimate Glucose in Diabetic Subjects During Dynamic Conditions

The purpose of this study was to determine whether an approach of multisensor technology with integrated data analysis in an armband system (SenseWear® Pro Armband, SWA) can provide estimates of plasma glucose concentration in diabetes. In all, 41 subjects with diabetes participated. On day 1 subjects underwent an oral glucose tolerance test (OGTT) and on day 2 a 60-minute treadmill test (TT). SWA plasma glucose estimates were compared against reference peripheral venous glucose concentrations. A continuous glucose monitoring device (CGM) was also placed on each subject to serve as a reference for clinical comparison. Pearson coefficient, Clarke error grid (CEG), and mean absolute relative difference (MARD) analyses were used to compare the performance of plasma glucose estimation. There were significant correlations between plasma glucose concentrations estimated by the SWA and the reference plasma glucose concentration during the OGTT (r = .65, P < .05) and the TT (r = .91, P < .05). CEG analysis revealed that during the OGTT, 93% of plasma glucose concentration readings were in the clinically acceptable zone A+B for the SWA and 95% for the CGM. During the TT, the SWA had 96% of readings in zone A+B, compared to 97% for the CGM. During OGTTs, MARDs for the SWA and CGM were 26% and 18%, respectively. During TTs, MARDs were 16% and 12%, respectively. Plasma glucose concentration estimation by the SWA's noninvasive multisensor approach appears to be feasible and its performance in estimating glucose approaches that of a CGM. The success of this pilot study suggests that multisensor technology holds promising potential for the development of a wearable, noninvasive, painless glucose monitor.

A new-generation continuous glucose monitoring system: improved accuracy and reliability compared with a previous-generation system

BACKGROUND

Use of continuous glucose monitoring (CGM) systems can improve glycemic control, but widespread adoption of CGM utilization has been limited, in part because of real and perceived problems with accuracy and reliability. This study compared accuracy and performance metrics for a new-generation CGM system with those of a previous-generation device.

SUBJECTS AND METHODS

Subjects were enrolled in a 7-day, open-label, multicenter pivotal study. Sensor readings were compared with venous YSI measurements (blood glucose analyzer from YSI Inc., Yellow Springs, OH) every 15 min (±5 min) during in-clinic visits. The aggregate and individual sensor accuracy and reliability of a new CGM system, the Dexcom(®) (San Diego, CA) G4™ PLATINUM (DG4P), were compared with those of the previous CGM system, the Dexcom SEVEN(®) PLUS (DSP).

RESULTS

Both study design and subject characteristics were similar. The aggregate mean absolute relative difference (MARD) for DG4P was 13% compared with 16% for DSP (P<0.0001), and 82% of DG4P readings were within ± 20 mg/dL (for YSI ≤ 80 mg/dL) or 20% of YSI values (for YSI >80 mg/dL) compared with 76% for DSP (P<0.001). Ninety percent of the DG4P sensors had an individual MARD ≤ 20% compared with only 76% of DSP sensors (P=0.015). Half of DG4P sensors had a MARD less than 12.5% compared with 14% for the DSP sensors (P=0.028). The mean absolute difference for biochemical hypoglycemia (YSI <70 mg/dL) for DG4P was 11 mg/dL compared with 16 mg/dL for DSP (P<0.001).

CONCLUSIONS

The performance of DG4P was significantly improved compared with that of DSP, which may increase routine clinical use of CGM and improve patient outcomes.

Accuracy and reliability of continuous glucose monitoring systems: a head-to-head comparison

OBJECTIVE

This study assessed the accuracy and reliability of three continuous glucose monitoring (CGM) systems.

RESEARCH DESIGN AND METHODS

We studied the Animas® (West Chester, PA) Vibe™ with Dexcom® (San Diego, CA) G4™ version A sensor (G4A), the Abbott Diabetes Care (Alameda, CA) Freestyle® Navigator I (NAV), and the Medtronic (Northridge, CA) Paradigm® with Enlite™ sensor (ENL) in 20 patients with type 1 diabetes mellitus. All systems were investigated both in a clinical research center (CRC) and at home. In the CRC, patients received a meal with a delayed and increased insulin dose to induce a postprandial glucose peak and nadir. Hereafter, randomization determined which two of the three systems would be worn at home until the end of functioning, attempting use beyond manufacturer-specified lifetime. Patients performed at least five reference finger sticks per day. An analysis of variance was performed on all data points ≥15 min apart.

RESULTS

Overall average mean absolute relative difference (MARD) (SD) measured at the CRC was 16.5% (14.3%) for NAV and 16.4% (15.6%) for ENL, outperforming G4A at 20.5% (18.2%) (P<0.001). Overall MARD when assessed at home was 14.5% (16.7%) for NAV and 16.5 (18.8%) for G4A, outperforming ENL at 18.9% (23.6%) (P=0.006). Median time until end of functioning was similar: 10.0 (1.0) days for G4A, 8.0 (3.5) days for NAV, and 8.0 (1.5) days for ENL (P=0.119).

CONCLUSIONS

In the CRC, G4A was less accurate than NAV and ENL sensors, which seemed comparable. However, at home, ENL was less accurate than NAV and G4A. Moreover, CGM systems often show sufficient accuracy to be used beyond manufacturer-specified lifetime.

Performance evaluations of continuous glucose monitoring systems: precision absolute relative deviation is part of the assessment

BACKGROUND

Even though a Clinical and Laboratory Standards Institute proposal exists on the design of studies and performance criteria for continuous glucose monitoring (CGM) systems, it has not yet led to a consistent evaluation of different systems, as no consensus has been reached on the reference method to evaluate them or on acceptance levels. As a consequence, performance assessment of CGM systems tends to be inconclusive, and a comparison of the outcome of different studies is difficult.

MATERIALS AND METHODS

Published information and available data (as presented in this issue of Journal of Diabetes Science and Technology by Freckmann and coauthors) are used to assess the suitability of several frequently used methods [International Organization for Standardization, continuous glucose error grid analysis, mean absolute relative deviation (MARD), precision absolute relative deviation (PARD)] when assessing performance of CGM systems in terms of accuracy and precision.

RESULTS

The combined use of MARD and PARD seems to allow for better characterization of sensor performance. The use of different quantities for calibration and evaluation, e.g., capillary blood using a blood glucose (BG) meter versus venous blood using a laboratory measurement, introduces an additional error source. Using BG values measured in more or less large intervals as the only reference leads to a significant loss of information in comparison with the continuous sensor signal and possibly to an erroneous estimation of sensor performance during swings. Both can be improved using data from two identical CGM sensors worn by the same patient in parallel.

CONCLUSIONS

Evaluation of CGM performance studies should follow an identical study design, including sufficient swings in glycemia. At least a part of the study participants should wear two identical CGM sensors in parallel. All data available should be used for evaluation, both by MARD and PARD, a good PARD value being a precondition to trust a good MARD value. Results should be analyzed and presented separately for clinically different categories, e.g., hypoglycemia, exercise, or night and day.

Evaluation of the performance of a novel system for continuous glucose monitoring

BACKGROUND

The performance of a continuous glucose monitoring (CGM) system in the early stage of development was assessed in an inpatient setting that simulates daily life conditions of people with diabetes. Performance was evaluated at low glycemic, euglycemic, and high glycemic ranges as well as during phases with rapid glucose excursions.

METHODS

Each of the 30 participants with type 1 diabetes (15 female, age 47 ± 12 years, hemoglobin A1c 7.7% ± 1.3%) wore two sensors of the prototype system in parallel for 7 days. Capillary blood samples were measured at least 16 times per day (at least 15 times per daytime and at least once per night). On two subsequent study days, glucose excursions were induced. For performance evaluation, the mean absolute relative difference (MARD) between CGM readings and paired capillary blood glucose readings and precision absolute relative difference (PARD), i.e., differences between paired CGM readings were calculated.

RESULTS

Overall aggregated MARD was 9.2% and overall aggregated PARD was 7.5%. During induced glucose excursions, MARD was 10.9% and PARD was 7.8%. Lowest MARD (8.5%) and lowest PARD (6.4%) were observed in the high glycemic range (euglycemic range, MARD 9.1% and PARD 7.4%; low glycemic range, MARD 12.3% and PARD 12.4%).

CONCLUSIONS

The performance of this prototype CGM system was, particularly in the hypoglycemic range and during phases with rapid glucose fluctuations, better than performance data reported for other commercially available systems. In addition, performance of this prototype sensor was noticeably constant over the whole study period. This prototype system is not yet approved, and performance of this CGM system needs to be further assessed in clinical studies.

Performance evaluation of a continuous glucose monitoring system under conditions similar to daily life

BACKGROUND

This study aimed at evaluating and comparing the performance of a new generation of continuous glucose monitoring (CGM) system versus other CGM systems, under daily lifelike conditions.

METHODS

A total of 10 subjects (7 female) were enrolled in this study. Each subject wore two Dexcom G4™ CGM systems in parallel for the sensor lifetime specified by the manufacturer (7 days) to allow assessment of sensor-to-sensor precision. Capillary blood glucose (BG) measurements were performed at least once per hour during daytime and once at night. Glucose excursions were induced on two occasions. Performance was assessed by calculating the mean absolute relative difference (MARD) between CGM readings and paired capillary BG readings and precision absolute relative difference (PARD), i.e., differences between paired CGM readings.

RESULTS

Overall aggregate MARD was 11.0% (n = 2392). Aggregate MARD for BG &lt;70 mg/dl was 13.7%; for BG between 70 and 180 mg/dl, MARD was 11.4%; and for BG &gt;180 mg/dl, MARD was 8.5%. Aggregate PARD was 7.3%, improving from 11.6% on day 1 to 5.2% on day 7.

CONCLUSIONS

The Dexcom G4 CGM system showed good overall MARD compared with results reported for other commercially available CGM systems. In the hypoglycemic range, where CGM performance is often reported to be low, the Dexcom G4 CGM system achieved better MARD than that reported for other CGM systems in the hypoglycemic range. In the hyperglycemic range, the MARD was comparable to that reported for other CGM systems, whereas during induced glucose excursions, the MARD was similar or slightly worse than that reported for other CGM systems. Overall PARD was 7.3%, improving markedly with sensor life time.

Performance evaluation of three continuous glucose monitoring systems: comparison of six sensors per subject in parallel

BACKGROUND

This study is aimed at comparing the performance of three continuous glucose monitoring (CGM) systems following the Clinical and Laboratory Standards Institute's POCT05-A guideline, which provides recommendations for performance evaluation of CGM systems.

METHODS

A total of 12 subjects with type 1 diabetes were enrolled in this study. Each subject wore six CGM systems in parallel, two sensors of each CGM system [FreeStyle Navigator™ (Navigator), MiniMed Guardian® REAL-Time with Enlite sensor (Guardian), DexCom™ Seven® Plus 3rd generation (Seven Plus)]. Each sensor was used for the lifetime specified by the manufacturer. To follow POCT05-A recommendations, glucose excursions were induced on two separate occasions, and venous and capillary blood glucose (BG) concentrations were obtained every 15 min for five consecutive hours. Capillary BG concentrations were measured at least once per hour during the day and once at night. Parameters investigated were CGM-to-BG differences [mean absolute relative difference (MARD)] and sensor-to-sensor differences [precision absolute relative difference (PARD)].

RESULTS

Compared with capillary BG reference readings, the Navigator showed the lowest MARD, with 12.1% overall and 24.6% in the hypoglycemic range; for the Guardian and the Seven Plus, MARD was 16.2%/34.9% and 16.3%/32.7%, respectively. PARD also was lowest for the Navigator (9.6%/9.8%), followed by the Seven Plus (16.7%/25.5%) and the Guardian (18.1%/20.2%). During induced glucose excursions, MARD between CGM and BG was, again, lowest for the Navigator (14.3%), followed by the Seven Plus (15.8%) and the Guardian (19.2%).

CONCLUSIONS

In this study, two sensors of each of the three CGM systems were compared in a setting following POCT05-A recommendations. The Navigator CGM system achieved more accurate results than the Guardian or the Seven Plus with respect to MARD and PARD. Performance in the hypoglycemic range was markedly worse for all CGM systems when compared with BG results.

First clinical evaluation of a new percutaneous optical fiber glucose sensor for continuous glucose monitoring in diabetes

BACKGROUND

This article describes a new fiber-coupled, percutaneous fluorescent continuous glucose monitoring (CGM) system that has shown 14 days of functionality in a human clinical trial.

METHOD

The new optical CGM system (FiberSense) consists of a transdermal polymer optical fiber containing a biochemical glucose sensor and a small fluorescence photometer optically coupled to the fiber. The glucose-sensitive optical fiber was implanted in abdominal and upper-arm subcutaneous tissue of six diabetes patients and remained there for up to 14 days. The performance of the system was monitored during six visits to the study center during the trial. Blood glucose changes were induced by oral carbohydrate intake and insulin injections, and capillary blood glucose samples were obtained from the finger tip. The data were analyzed using linear regression and the consensus error grid analysis.

RESULTS

The FiberSense worn at the upper arm exhibited excellent results during 14 wearing days, with an overall mean absolute relative difference (MARD) of 8.3% and 94.6% of the data in zone A of the consensus error grid. At the abdominal application site, FiberSense resulted in a MARD of 11.4 %, with 93.8% of the data in zone A.

CONCLUSIONS

The FiberSense CGM system provided consistent, reliable measurements of subcutaneous glucose levels in human clinical trial patients with diabetes for up to 14 days.

Towards smart tattoos: implantable biosensors for continuous glucose monitoring

Diabetes can strike at any age, from childhood to adulthood, and lasts a lifetime. Thus, it is important to find ways to increase the quality of life for diabetic patients through intensive, continuous care of blood glucose concentrations. Glucose biosensors that are implanted under the skin are promising for continuous glucose monitoring because they can constantly read blood glucose concentrations and signal a warning in case of hypo- or hyperglycemia. The demand for subcutaneous glucose biosensors has led to the development of many glucose-sensing principles and sensor designs. This Review covers the effort to develop subcutaneous glucose biosensors, including the glucose-sensing principles, and discusses their current status for in vivo monitoring. In addition, the Review examines the future prospects for intensive diabetes care.

Design of the health monitoring system for the artificial pancreas: low glucose prediction module

BACKGROUND

The purpose of this study was to design and evaluate a safety system for the artificial pancreas device system (APDS). Safe operation of the APDS is a critical task, where the safety system is engaged only as needed to ensure reliable operation without positive feedback to the controller.

METHODS

The Health Monitoring System (HMS) was designed as a modular system to ensure the safety of the APDS and the user. It was designed using a large set of ambulatory data and evaluated in silico by inducing hypoglycemia with a missed meal [bolus for a 65 g carbohydrate (CHO) meal] and administering rescue CHOs per HMS alerting. The HMS was validated in-clinic with a real-life challenge of a subject who overdosed insulin prior to admission.

RESULTS

The HMS was evaluated for clinical use with a 15 min prediction horizon. Retrospectively, 93.5% of episodes were detected with 2.9 false alarms per day. During in silico evaluation, the HMS reduced the time spent <70 mg/dl from 15% to 3%. When the HMS was first tested in-clinic, the subject overdosed ~3 U of insulin prior to her arrival to a closed-loop session (against protocol). The controller reduced insulin delivery, and the HMS gave four alerts that were successfully received via clinical software and text and multimedia messages. Even with insulin reduction and CHO supplements, hypoglycemia was unavoidable but manageable due to the HMS, confirming that a safety system to detect adverse events is an essential part of the APDS.

CONCLUSIONS

The ability of the HMS to be an effective alert system that provides a safety layer to the APDS controller has been demonstrated in a clinical setting.

Pilot study of a prototype minimally invasive intradermal continuous glucose monitor

INTRODUCTION

The purpose of this study was to assess point accuracy, rate-of-change accuracy, and safety of a prototype, minimally invasive continuous glucose monitoring (CGM) device over a 12 h in-clinic study. The CGM system consisted of a wireless electronics module with a disposable glucose sensor attached to the bottom. The electronics module was affixed to the abdomen using an adhesive pad on the bottom of the disposable sensor housing.

METHODS

Two CGM sensors were inserted into the abdominal tissue (left and right) of 15 adults aged 26-67 years, 5 with normoglycemia, 5 with type 1 diabetes, and 5 with type 2 diabetes. Over a 12 h period, each participant was fed three meals. Reference venous blood samples were drawn at periodic intervals (12.4 ± 5.3 min), and glucose was measured at the bedside using a laboratory reference method. For each participant, a single plasma equivalent glucose concentration was used for retrospective sensor calibration.

RESULTS

A total of 1082 paired data points were obtained from 15 subjects and 25 of 30 sensors. Statistical analysis yielded a mean absolute relative difference of 12.6% and a mean absolute difference of 16.0 mg/dl. Continuous glucose error grid analysis showed the combined point and rate-of-change accuracy was 97.4% in zone A and 1.5% in zone B (98.9% A+B), with 1.1% erroneous readings.

CONCLUSIONS

The prototype CGM system provided clinically accurate results 98.9% of the time and was well tolerated by participants, with little or no pain and no adverse events.

Continuous glucose monitoring accuracy results vary between assessment at home and assessment at the clinical research center

BACKGROUND

Continuous glucose monitoring system (CGMS) accuracy is of critical importance both in delivering therapeutic value and as a component of a closed-loop system. This study aims at assessing the differences between accuracy assessments of CGMS at home and at the clinical research center (CRC).

METHODS

Twelve patients with type 1 diabetes used the Dexcom® SEVEN® PLUS (DexCom, Inc.) CGMS for 7 days. Patients performed ≥6 finger pricks [self-measurement of blood glucose (SMBG)] per day while at home. Reference blood glucose measurements were taken during a 24 h CRC admission (YSI 2300 STAT Plus™). Continuous glucose monitoring system data were compared with YSI and SMBG values. Outcome measures included mean absolute relative difference (MARD) and Clarke error grid analysis (CEGA).

RESULTS

During CRC admission, the MARD of CGMS vs YSI glucose values was 19.2% (n = 509)--significantly higher than 16.8% at home (n = 611) (p = .004). In the hypoglycemic range, MARD was 23.9% at CRC (n = 26)--not significantly different from 41.6% at home (n = 39) (p = .269). In the hyperglycemic range, CRC MARD at 20.3% (n = 115) was significantly higher than home MARD at 11.2% (n = 118) (p = .001). Clarke error grid analysis showed no significant difference in distribution of data pairs (overall p = .317).

CONCLUSIONS

This study illustrates the importance of the setting used when assessing CGMS accuracy. Continuous glucose monitoring system accuracy at home appeared better than at the CRC. This is probably due to the higher sampling rate of reference measurements, feasible only in the CRC. Testing CGMS accuracy in the CRC provides valuable information over and above home testing.

The accuracy and efficacy of real-time continuous glucose monitoring sensor in Chinese diabetes patients: a multicenter study

BACKGROUND

The purpose of this multicenter study was to investigate the accuracy of a real-time continuous glucose monitoring sensor in Chinese diabetes patients.

SUBJECTS AND METHODS

In total, 48 patients with type 1 or 2 diabetes from three centers in China were included in the study. The MiniMed Paradigm(®) 722 insulin pump (Medtronic, Northridge, CA) was used to monitor the real-time continuous changes of blood glucose levels for three successive days. Venous blood of the subjects was randomly collected every 15 min for seven consecutive hours on the day when the subjects were wearing the sensor. Reference values were provided by the YSI(®) 2300 STAT PLUS™ glucose and lactate analyzer (YSI Life Sciences, Yellow Springs, OH).

RESULTS

In total, 1,317 paired YSI-sensor values were collected from the 48 patients. Of the sensor readings, 88.3% (95% confidence interval, 0.84-0.92) were within±20% of the YSI values, and 95.7% were within±30% of the YSI values. Clarke and consensus error grid analyses showed that the ratios of the YSI-sensor values in Zone A to the values in Zone B were 99.1% and 99.9%, respectively. Continuous error grid analysis showed that the ratios of the YSI-sensor values in the region of accurate reading, benign errors, and erroneous reading were 96.4%, 1.8%, and 1.8%, respectively. The mean absolute relative difference (ARD) for all subjects was 10.4%, and the median ARD was 7.8%. Bland-Altman analysis detected a mean blood glucose level of 3.84 mg/dL. Trend analysis revealed that 86.1% of the difference of the rates of change between the YSI values and the sensor readings occurred within the range of 1 mg/dL/min.

CONCLUSIONS

The Paradigm insulin pump has high accuracy in both monitoring the real-time continuous changes and predicting the trend of changes in blood glucose level. However, actual clinical manifestations should be taken into account for diagnosis of hypoglycemia.

Accuracy of the Enlite 6-day glucose sensor with guardian and Veo calibration algorithms

OBJECTIVE

This study investigates the accuracy of a newly developed, next-generation subcutaneous glucose sensor, evaluated for 6-day use.

RESEARCH DESIGN AND METHODS

Seventy-nine subjects (53 men, 26 women) with type 1 diabetes and 18 subjects (14 men, four women) with type 2 diabetes completed a three-center, prospective, sensor accuracy study. The mean age for the group was 42.2±15.0 years (mean±SD), ranging from 18 to 71 years, with a mean glycosylated hemoglobin level of 7.6±1.5%, ranging from 5.5% to 14%. Subjects wore Enlite™ sensors (Medtronic Diabetes, Northridge, CA) in the abdominal and buttocks region for two separate 7-day periods and calibrated with a home-use blood glucose meter. Subjects participated in an in-clinic testing day where frequent sampled plasma glucose samples were acquired every 15 min for 10 h. Sensor data was retrospectively processed with Guardian(®) REAL-Time (Medtronic) and Paradigm(®) Veo™ (Medtronic) calibration routines, and accuracy metrics were calculated for each algorithm and sensor location. Physiological time lag for each measurement site was calculated.

RESULTS

Based on 6,404 plasma-sensor glucose paired points, the Enlite sensor with Veo calibration algorithm produced a mean absolute relative difference of 13.86% with 97.3% of points within the A+B zones of the Clarke error grid. Threshold-only alarms detected 90.1% of hypoglycemia and 90% of hyperglycemia. Mean time lag measured at the abdominal region was 7.94±6.48 min compared with 11.70±6.71 min (P<0.0001) at the buttocks area.

CONCLUSIONS

The Enlite sensor accurately measures glucose when compared with gold standard laboratory measurements over its 6-day use. Sensors placed in the buttocks region exhibited greater time lags than sensors placed in the abdomen.

A multiple local models approach to accuracy improvement in continuous glucose monitoring

BACKGROUND

Continuous glucose monitoring (CGM) devices estimate plasma glucose (PG) from measurements in compartments alternative to blood. The accuracy of currently available CGM is yet unsatisfactory and may depend on the implemented calibration algorithms, which do not compensate adequately for the differences of glucose dynamics between the compartments. Here we propose and validate an innovative calibration algorithm for the improvement of CGM performance.

METHODS

CGM data from GlucoDay(®) (A. Menarini, Florence, Italy) and paired reference PG have been obtained from eight subjects without diabetes during eu-, hypo-, and hyperglycemic hyperinsulinemic clamps. A calibration algorithm based on a dynamic global model (GM) of the relationship between PG and CGM in the interstitial space has been obtained. The GM is composed by independent local models (LMs) weighted and added. LMs are defined by a combination of inputs from the CGM and by a validity function, so that each LM represents to a variable extent a different metabolic condition and/or sensor-subject interaction. The inputs best suited for glucose estimation were the sensor current I and glucose estimations Ĝ, at different time instants [I(k), I(k)(-1), Ĝ(k)(-1)] (IIG). In addition to IIG, other inputs have been used to obtain the GM, achieving different configurations of the calibration algorithm.

RESULTS

Even in its simplest configuration considering only IIG, the new calibration algorithm improved the accuracy of the estimations compared with the manufacturer's estimate: mean absolute relative difference (MARD)=10.8±1.5% versus 14.7±5.4%, respectively (P=0.012, by analysis of variance). When additional exogenous signals were considered, the MARD improved further (7.8±2.6%, P<0.05).

CONCLUSIONS

The LM technique allows for the identification of intercompartmental glucose dynamics. Inclusion of these dynamics into the calibration algorithm improves the accuracy of PG estimations.

Continuous glucose monitoring considerations for the development of a closed-loop artificial pancreas system

BACKGROUND

Commercialization of a closed-loop artificial pancreas system that employs continuous subcutaneous insulin infusion and interstitial fluid glucose sensing has been encumbered by state-of-the-art technology. Continuous glucose monitoring (CGM) devices with improved accuracy could significantly advance development efforts. However, the current accuracy of CGM devices might be adequate for closed-loop control.

METHODS

The influence that known CGM limitations have on closed-loop control was investigated by integrating sources of sensor inaccuracy with the University of Virginia Padova Diabetes simulator. Non-glucose interference, physiological time lag and sensor error measurements, selected from 83 Enlite™ glucose sensor recordings with the Guardian® REAL-Time system, were used to modulate simulated plasma glucose signals. The effect of sensor accuracy on closed-loop controller performance was evaluated in silico, and contrasted with closed-loop clinical studies during the nocturnal control period.

RESULTS

Based on n = 2472 reference points, a mean sensor error of 14% with physiological time lags of 3.28 ± 4.62 min (max 13.2 min) was calculated for simulation. Sensor bias reduced time in target for both simulation and clinical experiments. In simulation, additive error increased time <70 mg/dl and >180 mg/dl by 0.2% and 5.6%, respectively. In-clinic, the greatest low blood glucose index values (max = 5.9) corresponded to sensor performance.

CONCLUSION

Sensors have sufficient accuracy for closed-loop control, however, algorithms are necessary to effectively calibrate and detect erroneous calibrations and failing sensors. Clinical closed-loop data suggest that control with a higher target of 140 mg/dl during the nocturnal period could significantly reduce the risk for hypoglycemia.

Comparison of point-of-care testing glucose results from intensive care patients measured with network-ready devices

BACKGROUND

Fast and reliable glycemic control is of tremendous importance in intensive care units. Point-of-care devices used in professional care have to be precise and of low variability, and their connectivity has to outrange the abilities of home-care equivalents. In particular, the meter's efficiency should be tested not only with spiked blood samples from healthy donors but also with blood from intensive care unit patients because of their special matrix conditions as low hematocrit, oxygen pressure variability, or medication.

METHODS

Four types of network-ready glucose meters were tested. Data, obtained from native or maltose/xylose-spiked intensive care patients' blood, were compared (oxygen, hematocrit, glucose, and maltose and xylose dependencies) with those from a YSI 2300 STAT Plus™ glucose and lactate analyzer (YSI Life Sciences, Yellow Springs, OH). According to ISO 15197 (2003) acceptance of glucose meter results was determined. Quality control results were investigated considering a new calculation type in German guidelines.

RESULTS

Three of the meters fulfill the overall acceptance criterions. Two of the meters achieved accuracies above 93% in all oxygen, hematocrit, and glucose subgroups. Maltose generates deviations leading to accuracies from 71.1% to 100%, and xylose causes accuracies of 33.3% to 100%.

CONCLUSIONS

State of the art for manufacturing small network point-of-care testing glucose meters has reached a new level of precision, but the devices still have to be handled with care, and in particular the staff of an intensive care unit still needs knowledge about possible interferences.

Continuous blood glucose monitoring in diabetes treatment

Continuous glucose monitoring system (CGMS) is a developing technology in the field of diabetes treatment since it enables patients to effectively control and adjust their insulin therapy. Clinical trials have shown its efficacy in lowering HbAlc significantly especially in adults with type 1 diabetes and those with HbAlc >8%. Improvement is sustained for at least one year. Conflicting data exist for children. Most recent studies agree that the nearly daily use of CGMS is accompanied by significant lowering of HbA1c independent of age. However, the randomized clinical trials have shown that the use of CGMS does not reduce significantly the number of severe hypoglycemic episodes as it is expected, but recent data indicate that it reduces the time spent in hypoglycemia. Accuracy remains a key issue for CGMS, particularly in children and adolescents who may have increased variability of blood glucose. CGMS cost is another barrier to the everyday use since reimbursement of CGM is limited to a few countries only. This review will focus on the present status of the use of CGMS in type 1 diabetes (T1D) patients.